Objective The Junggar Basin is recognized as a significant petroliferous basin in China, and its hydrocarbon exploration targets have shifted to deeper strata. However, the 3D seismic data of this basin suffer from low signal-to-noise ratios (SNRs) and high data volumes due to the basin's complex near-surface conditions, the great depths of exploration targets, and seismic data acquisition methods characterized by wide azimuths, broadbands, and high density. This complicates the identification of hydrocarbon exploration targets, rendering the improvement in the quality of the 3D seismic data by noise suppression vitally important.
Methods The progress in the deep learning theory and the enhancement of hardware performance have significantly boosted the learning capability and processing efficiency of deep neural networks. Based on residual learning and batch normalization techniques, this study developed a three-dimensional denoising convolutional neural network (3D-DnCNN) and a deep learning-based noise suppression workflow applicable to the 3D seismic data of the Junggar Basin.
Results and Conclusion To meet the actual demand of a large contiguous surveyed area in the Junggar Basin, high-quality labels were constructed using the noise suppression results of zones with high seismic coverage and SNRs, and the trained 3D-DnCNN was then applied to the entire study area. Compared to the conventional industrial workflow, the workflow developed in this study yielded more consistent seismic events, more intact faults preserved, and clearer top boundary and inner layers of the Carboniferous strata. Additionally, since the 3D-DnCNN learned the characteristics of offset-related arc noise in high-SNR zones, it outperformed the conventional industrial workflow in suppressing such noise across the entire surveyed area. By adjusting network parameters such as the network depth, convolution kernel size, and the strategy for selecting training samples, the 3D-DnCNN can be further optimized to adapt to seismic data from different areas, thereby enhancing the applicability and effectiveness of the seismic noise suppression technique.
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